Applying Fuzzy Linear Programming to Supply Chain Planning with Demand, Process and Supply Uncertainty

Abstract

A Supply Chain (SC) is a dynamic network of several business entities that involve a high degree of imprecision. This is mainly due to its real-world character where uncertainties in the activities extending from the suppliers to the customers make SC imprecise (Fazel Zarandi et al., 2002). Several authors have analysed the sources of uncertainty present in a SC, readers are referred to Peidro et al. (2008) for a review. The majority of the authors studied (Childerhouse & Towill, 2002; Davis, 1993; Ho et al., 2005; Lee & Billington, 1993; MasonJones & Towill, 1998; Wang & Shu, 2005), classified the sources of uncertainty into three groups: demand, process/manufacturing and supply. Uncertainty in supply is caused by the variability brought about by how the supplier operates because of the faults or delays in the supplier’s deliveries. Uncertainty in the process is a result of the poorly reliable production process due to, for example, machine hold-ups. Finally, demand uncertainty, according to Davis (Davis, 1993), is the most important of the three, and is presented as a volatility demand or as inexact forecasting demands. The coordination and integration of key business activities undertaken by an enterprise, from the procurement of raw materials to the distribution of the end products to the customer, are concerned with the SC planning process (Gupta & Maranas, 2003), one of the most important processes within the SC management concept. However, the complex nature and dynamics of the relationships among the different actors imply an important degree of uncertainty in the planning decisions. In SC planning decision processes, uncertainty is a main factor that can influence the effectiveness of the configuration and coordination of supply chains (Davis, 1993; Jung et al., 2004; Minegishi & Thiel, 2000) and tends to propagate up and down along the SC, affecting its performance appreciably (Bhatnagar & Sohal, 2005). Most of the SC planning research (Alonso-Ayuso et al., 2003; Guillen et al., 2005; Gupta y Maranas, 2003; Lababidi et al., 2004; Santoso et al., 2005; Sodhi, 2005) models SC uncertainties with probability distributions that are usually predicted from historical data. However, whenever statistical data are unreliable or are even not available, stochastic models may not be the best choice (Wang y Shu, 2005). The fuzzy set theory(Zadeh, 1965) O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg